Image information extraction is the key technology in image registration and recognition. The effect of image information extraction tends to directly determine the effect of image registration and recognition. Therefore, how to extract image information with a strong expression capability from the image is a research focus in the field of image processing technology. In this regard, the feature point descriptor can describe the change between a feature point and its adjacent pixel, and may retain the invariant feature of the feature point (for example, the invariability in rotation or scale). Therefore, the feature point descriptor as image information with a strong expression capability has been widely applied in the field of image processing technology.
However, the existing method for generating a feature point descriptor for training a model is usually to first generate a target image by projective transforming a reference image, and then acquire coordinates of each neighborhood pixel of a feature point from the target image to generate the feature point descriptor. Generally it is necessary to perform projective transformation to all pixels in the reference image to generate the target image during the process of generating the feature point descriptor, thus causing a high computational complexity for generating the feature point descriptor.